In modern homogeneous and heterogeneous cellular network topologies, large numbers of base stations, operating on similar, licensed frequency spectra, are being utilized by network access providers to accommodate a growing demand for increased network capacity. In networks where neighboring network cells have significant, overlapping wireless coverage areas, it is particularly important for service providers to be able to accurately determine which network resources should be allocated to handle various service assignment tasks for particular groups of network subscribers residing within the overlapping coverage areas. Subscriber diversity in this context can lead to unique network resource consumption patterns amongst regional network areas where traffic densities may vary widely throughout the course of any particular day on a time-varying basis.
Further, commercial cellular deployments are increasingly being used to provide a larger breadth of digital communications services to various types of distributed network clientele. For example, many users in particular regions of a network have access to relatively high network throughput service, associated with enhanced data-rate plans (high bandwidth access provider service offerings). These users may utilize local network resources to transfer large amounts of Internet-based data to and from their cellular communications device(s) over the course of a single day. Other wireless subscribers, with lesser available network service, may use local network resources primarily for voice data communications. As would be understood by those skilled in the Art, network throughput is generally defined as an average rate of successful data communications delivery over a particular network communication channel per unit of time. This throughput is usually measured in bits per second (bps) or alternately in data packets per second.
In modern wireless communications networks, it is particularly important for service providers to be able to accurately predict or forecast their regional service subscribers' network resource consumption patterns, in order to be able to effectively determine which network users should be assigned to various overlapping serving cells within particular network sectors at specific times during a day, as network traffic loads fluctuate. In light of this user load variance, improved radio channel capacity assignment and network resource allocation solutions are becoming crucial instruments that service providers must utilize to compensate for a dynamically changing demands of service capacity, caused by network subscriber diversity and associated bandwidth consumption habits in various regions of a data communications network.
Service capacity, as viewed in this context, generally refers to the number and type of users that a network cell's limited radio resources (e.g., a radio channel(s), backhaul link, processing unit(s), scheduler, etc.) can support while providing expected levels of service to its subscribers (e.g., as designated in service level agreements for active network users). As will be made more apparent herein, network capacity, while simple in concept, often depends on a complex and large sets of dynamically changing factors that can be cumbersome to calculate and difficult for service providers to accurately predict using existing methods for modeling capacity planning solutions, and largely manual, radio resource compensation techniques.
As the number of active users in a particular wireless communications network increase, it becomes more and more important to properly manage radio frequency resources that are shared amongst regional network cells, particularly in networks employing frequency reuse assignment (e.g., a majority of LTE™, LTE Advanced™, GSM™, UMTS™, and Wi-Max™ based networks). By way of example, cells with overlapping coverage areas might share a fixed number of wireless communication channels, and on any given day, a particular network cell may experience detrimentally reduced network capacity, based on heavy subscriber usage of its limited network resources (e.g., available communications channel bandwidth). This heavy resource usage may correspond with a particular time of day, geographic location, serving cell technology, aggregate network user type(s), etc.
Therefore, it would be desirable to be able to more efficiently allocate network resources amongst multiple regional network resources (e.g., neighboring network base stations) having overlapping coverage areas, depending on actual and/or expected (forecast) usage demand for each local network resource. This would help to reduce network congestion problems at overburdened network cells, and it would accordingly also improve a network service provider networks' Quality of Service (QOS) as well as network service subscribers' collective Quality of Experience (QOE) within high traffic areas of a data communications network. Negative effects associated with poor QOS and poor QOE (e.g., conditions largely caused by congestion and/or interference), which can be mitigated by optimizing network resource allocation using improved network resource allocation processes, may include: queuing delay, data loss, as well as blocking of new and existing network connections for certain network subscribers.
Prior network resource allocation solutions do not adequately account for network capacity relating to aggregate network subscriber usage metrics. Existing static channel assignment and network resource allocation solutions may fail to adequately provision a network, such that congestion in areas of cell coverage can occur during peak usage periods (e.g., hourly periods with high traffic loading). Further, these existing solutions must err on the conservative side in order to reduce the probability of congestion and co-channel interference between and amongst neighboring network cells. This can lead to situations where too few network resources are allocated in high traffic areas amongst a cluster of network base stations. In these scenarios, a service provider entity or network controller may not react quickly enough to avoid or largely mitigate various detrimental traffic overload scenarios. Existing dynamic channel assignment and network allocation solutions similarly allocate a pool of available communications channels based on short-sighted estimations of regional traffic patterns that are largely generic (e.g., high-level models, incorporating limited samples of real-world network resource consumption data) and do not factor in a sufficient number of variables relating to actual network usage, in order to adequately maximize network resource usage amongst clusters of overlapping network cells.
Accordingly, there remains a need for systems and methods that employ improved network resource allocation solutions that better compensate for user equipment loading trends at one or more network base stations. It would be helpful if these solutions accounted for both regional traffic loading patterns and actual resource consumption for various network subscriber types in specific network sectors of interest (e.g., in areas of overlapping network service). In this way, it would be easier for service providers to readily allocate network resources to network service subscribers in a time efficient manner, in dynamically changing network environments. It would also be helpful if these solutions took advantage of existing network resources, such that various network cells could autonomously determine their present user capacity and forecast their residual available user capacity based on their determined user capacity and/or historical user capacity information. In this scenario, it would also be advantageous if neighboring network cells could frequently communicate amongst each other to keep track of overlapping cell available capacities, in order to affect handover determinations and automatically balance network loads amongst regional network areas to improve a network's QOS and QOE metrics. These improved network optimization solutions would effectively reduce the level of required human intervention for successful network resource allocation operations. This in turn would result in operational savings for service providers, and it would provide for many other performance, quality, and operational benefits. The importance of these benefits would be readily understood by those familiar with the multitude of benefits commonly associated with self-organized network (SoN) solutions.